Genetic Algorithms for Multi-Objective Community Detection in Complex Networks

Abstract

AbstractCommunity detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure–function relationship. Therefore, detecting communities (or modules) can be a way to identify substructures that could correspond to important functions. Community detection can be viewed as an optimization problem in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the detection problem. However, those approaches have drawbacks because they attempt to optimize only one objective function, this results in a solution with a particular community structure property. More recently, researchers have viewed the community detection problem as a multi-objective optimization problem, and many approaches have been proposed. Genetic Algorithms (GA) have been used as an effective optimization technique to solve both single- and multi-objective community detection problems. However, the most appropriate objective functions to be used with each other are still under debate since many similar objective functions have been proposed over the years. We show how those objectives correlate, investigate their performance when they are used in both the single- and multi-objective GA, and determine the community structure properties they tend to produce. KeywordsSocial networksCommunity detectionGenetic algorithmsCommunities’ quality measures